Abstract:
An elevator system for up-peak servicing of a building having a dual lobby. The system includes a controller having an electronic processor coupled to a memory; a plurality of elevator cars controllably connected to the controller, a dual lobby routine stored within the memory, the dual lobby routine includes instructions for dispatching at least one of the elevator cars to a lower lobby during up-peak, indicating a sector assigned to the car, nudging (if needed) the car if a lower lobby time-out is exceeded, dispatching the car to the upper lobby if a load weight threshold is not exceeded, and then indicating the sector assigned to the car while the car is located at the upper lobby.
Abstract:
The invention relates to a procedure for controlling an elevator group. According to the invention, the landing calls issued from different floors are weighted by a floor-specific weight factor. The weighted call time is utilized in the calculation of the serving time of the calls and for the selection of the best elevator to serve a landing call.
Abstract:
The present invention is directed to determining an arrival time for each of the passengers boarding an elevator car. Where the elevator car stops at a floor in response to a hall call request, the arrival time of the passengers which boarded the elevator car is preferably determined based the time period between when the hall call was registered and when the elevator car door closed. Where the elevator car stops at a floor in response to a car call registered within the elevator, the arrival time of the passengers which boarded the elevator car is preferably determined based on the time period between when the elevator stopped at the floor and when the elevator car door closed. Alternatively, the time period between when the elevator car door opened and when the elevator car door closed can be used. In the preferred embodiment, the first passenger is assumed to have an arrival time corresponding to the beginning of the time period. If more than one passenger boarded the elevator, the passengers are assumed to have arrived in a distributed fashion over this time period.
Abstract:
A system for allocating hall calls in a group of elevators includes a plurality of neural network modules to model, learn and predict passenger arrival rates and passenger destination probabilities. The models learn the traffic occurring in a building by inputting to the neural networks traffic data previously stored. The neural networks then adjust their internal structure to make historic predictions based on data of the previous day and real time predictions based on data of the last ten minutes. The predictions of arrival rates are combined to provide optimum predictions. From every set of historic car calls and the optimum arrival rates, a matrix is constructed which stores entries representing the number of passengers with the same intended destination for each hall call. The traffic predictions are used separately or in combination by a group control to improve operating cost computations and car allocation, thereby reducing the travelling and waiting times of current and future passengers.
Abstract:
An elevator system (FIG. 1) employing a microprocessor-based group controller (FIG. 2) communicating with elevator cars (3,4, . . . ) to derive relative prediction values to be used in a scheme of ultimately assigning, e.g., cars to hall calls at a plurality of floors in the building, using appropriate dispatching strategies based on predicted traffic conditions. In the algorithm of the invention (FIG. 3) the building's inhabitants' behavior based on weekly events, daily events and real time events is predicted, with the events being used to derive relative prediction values based on the weighted summation of the real, daily and weekly values of the events. Weekly events are considered to be those that happened over a past number of weeks (e.g. 10 weeks) on the same day of the week; while daily events are those that happened over the past few days (e.g. 5 days). Real time events are those which are effectively currently happening, with the time frame of reference being sufficiently short to produce data which effectively can be considered "real time" data, typically covering only a matter of some minutes (e.g. 4 minutes) or even less. In determining what events recorded in the daily and weekly historic data bases are to be used in deriving the predicted relative values, the degree of the certainty of the data is evaluated using two threshold considerations, and historic factors based on data which fails to have the requisite certainty is not used in the predictions.
Abstract:
An elevator control apparatus capable of predicting reversion floors of elevator cages accurately. The control apparatus comprises a neural network, in which traffic state data are fetched into the neural network, so that predicted values of floors where the moving direction of each cage is reversed are calculated as predicted reversion floors. In the elevator control apparatus, reversion floors near true reversion floors can be predicted flexibly correspondingly to traffic state and traffic volume.
Abstract:
The invention relates to call registration wherein of a plurality of elevators an elevator which a user (passenger) wants to ride is called to the elevator hall of the user. A display is provided at an elevator hall for users. When a user depresses a hall calling button, a group supervisory controller determines and selects a suitable elevator, and causes the condition of the elevator to be displayed on the display in the form of sentences or the like. If the user is satisfied with the selected elevator, the hall calling button is not actuated again. If the user is dissatisfied with the selected elevator, the hall calling button is depressed again so that the controller selects another elevator for service to the user.
Abstract:
An apparatus for statistically processing elevator traffic information by statistically processing traffic information on each building floor in each of a number of time zones of a day to generate group control information, comprising a first statistical data generator device responsive to the traffic information on each floor for issuing a total value of the traffic information as a statistical value, a second statistical data generator device for issuing as a statistical value the ratio of the traffic information on each floor to the absolute value of traffic information, and a control converter device for converting the statistical values from the first and second statistical data generator devices into the group control information.
Abstract:
A demand estimation apparatus wherein one cycle of a demand fluctuating substantially cyclically is divided into a plurality of sections having predetermined time widths, the demand is measured by cumulating demand measurements taken a varying number of times for each section and assigning an increasing weighting parameter for successively newer measured values, an estimated demand for each section is calculated based on the measured values for the section and a weight coefficient, and the weight coefficient is changed in accordance with the number of times of cumulation of demand measurements.
Abstract:
An improved traffic demand analyzing system is disclosed in which the past traffic demand data that have occurred during the same predetermined unit time periods as the current time period are computed so that the past data may be implemented in the group control of the elevator operation. The system comprises a unit for measuring and storing the traffic volume for a plurality of predetermined time periods up to the current time period, and a unit for preestimating the traffic volume for the near future through placing greater emphasis on the traffic volume associated with said predetermined time period nearer to the current time or through making use of only traffic values associated with a certain number predetermined time periods near to the current time.